Wireless communication based on multiple antennas is a very promising technique which is subject to extensive investigations so as to take into advantage of the significant increase of data rate which may be obtained by such technique.
FIG. 1 illustrates a basic 2×2 multiple-Input Multiple Output (MIMO) spatial multiplexing communication between an emitter 10 and a receiver 20, and the processing of a single data flow represented by reference 1 which is divided into two distinctive data streams 2 and 3 by means of a multiplexer 15 and each subflows are then being processed by a respective modulator and RF circuit (resp. 13 and 14) before being conveyed to two transmit antennas 11 and 12.
On the receiver side, two antennas 21 and 22 provides two RF signals which are received by receiver 20 which performs RF reception, detection and then demodulation of the two data streams before demultiplexing it into one single data stream.
The MIMO configuration—with specific schemes—allows to get rid of the different obstacles (such as represented by obstacles 28 and 29).
Let us introduce a nT—transmit and nR—receive nT×nR MIMO system model such as: y=Hx+n, where y is the receive symbols vector, H the channel matrix, x the transmit symbols vector that is independently withdrawn from a constellation set ξ and n an additive white Gaussian noise. A well-known technique used for determining the optimal Maximum Likelihood (ML) estimate {circumflex over (x)}ML by avoiding an exhaustive search is based on the examination of the sole lattice points that lie inside a sphere of radius d. That technique is denoted as the Sphere Decoder (SD) technique and, starting from the ML equation
                                                                        x                ^                            ML                        =                                                            arg                  ⁢                                                                          ⁢                  min                                                  x                  ∈                                      ξ                                          n                      T                                                                                  ⁢                                                          ⁢                                                                                      y                    -                    Hx                                                                    2                                              ,                                          ⁢                      which            ⁢                                                  ⁢            reads            ⁢                          :                                      ⁢                                  ⁢                                            x              ^                        SD                    =                                                                      arg                  ⁢                                                                          ⁢                  min                                                  x                  ∈                                      ξ                                          n                      T                                                                                  ⁢                                                          ⁢                                                                                                                                    Q                        H                                            ⁢                      y                                        -                    Rx                                                                    2                                      ≤                          d              2                                                          (        1        )            where H=QR, with the classical QR Decomposition (QRD) definitions, and d is the sphere constraint.
The SD principle has been introduced and leads to numerous implementation problems. In particular, it is a NP-hard Non-deterministic Polynomial-time hard algorithm. This aspect has been partially solved through the introduction of an efficient solution that lies in a so-called Fixed Neighborhood Size Algorithm (FNSA)—commonly denoted as the K-Best—which offers a fixed complexity and possibilities of parallel implementation. However, this known technique leads the detector to be sub-optimal because of a loss of performance in comparison with the ML detector. It is particularly true in the case of an inappropriate K according to the MIMO channel condition number since, unfortunately, it might occur that the ML solution might be excluded from the search tree.
In the following of the description below, and since the complexity is fixed with such a detector, the exposed optimizations will induce a performance gain for a given Neighborhood size or a reduction of the Neighborhood size for a given Bit Error Rate (BER) goal. Some classical and well-known optimizations in the FNSA performance improvement lie in the use of the Sorted QRD (SQRD) at the preprocessing steps, the Schnorr-Euchner (SE) enumeration strategy and the dynamic K-Best at the detection step.
However, although a Neighborhood study remains the one and only solution that achieves near-ML performance, it may lead to the use of a large size Neighborhood scan that would correspond to a dramatic increase of the computational complexity. This point is particularly true in the case of high order modulations.
Also, the SD must be fully processed for each transmit symbols vector detection over a given channel realization. A computational complexity reduction by considering the correlation between adjacent-channel is not possible, even if the channel may be considered as constant over a certain block code size within the coherence band (time). Consequently, due to the SD's principle itself, the skilled man would have noticed the necessity of reducing the computational complexity of any SD-like detector for making it applicable in the LTE-A context.
Aiming at providing a low-complexity near-ML detector in the case of high modulation orders (16QAM, 64QAM), the Reduced Domain Neighborhood (RDN) Lattice-Reduction-Aided (LRA) K-Best has been disclosed in non published European patent application 10368044.3, entitled <<Detection process for a receiver of a wireless MIMO communication system>>, filed on 30 Nov. 2010 by the Applicant of the present application, and which is herein incorporated by simple reference.
The above mentioned non published application teaches a Neighborhood size limitation on the basis of a specific ML metric formulation that makes the SD apply a Neighborhood study in a modified constellation domain, a so-called Reduced Domain Neighborhood (RDN). However, the offered performance has been shown to be near-ML, but at the price of a large computational complexity in the QPSK case.
Because the technique which was described in the above mentioned European patent application requires a significant amount of system resources for the purpose of performing the appropriate Neighborhood search within the so-called Reduced Domain Neighborhood (RDN), there is a desire for performing a Neighborhood search with Original Domain Neighborhood (ODN) in some particular cases.
Such is the technical problem solved by the present invention.